Surface Urban Heat Island: A Comparative Study Between India and the United States

Global temperatures have risen by 0.18⁰C per decade and could rise further due to increasing anthropogenic activity in urban areas, which may host 68% of the world's population by 2050. Urban Heat Island (UHI), the higher urban temperatures compared to the rural surroundings, is

one of the most widely researched phenomena to study the impact of urbanization on climate. Yet, limited research exists across rapidly urbanizing countries, like India, which could be the biggest contributor to urban population growth in the following decades.

This research is a comparative examination of UHI and its association with the urban built environment in India and the US. It conducts a quantitative analysis of the Land Surface Temperatures (LST) and the Surface Urban Heat Island (SUHI) magnitude (ΔT) across 42 cities in India and 32 cities in the US using remote sensing data. Such large-scale multi-city analysis facilitated statistical analysis of the observed LST and ΔT.

The daytime analysis of LST and ΔT showed SUHI in the US but the reverse in India, where urban areas are cooler than rural surroundings. Rural LSTs in India were higher than urban LSTs, which are already 10-12⁰C higher than in the US. The dry or non-green vegetation land cover is linked to higher daytime rural LSTs in India. Further investigations showed that the popular remote sensing indices used to quantify built-up areas cannot differentiate between built-up areas, cropland, and other sparse vegetative land covers that are dominant in India. Literature on the thermal characteristics, especially the thermal admittance of non-green vegetation, dry soil, and barren land covers, indicates rapid warming of such land covers during the daytime and cooling after sunset. Consistent with this, the subsequent nighttime SUHI analysis showed warmer urban areas than rural areas in India. This nighttime SUHI magnitude is higher in India than in the US, and this difference is statistically significant. Together these findings highlight the shortcomings of conventional SUHI research methods in global analysis.

Since the conventional SUHI analysis methods and indices did not show an association between urban LSTs or ΔT with built-up areas and vegetation in India, this study used impervious surface area (ISA) data to evaluate the temporal changes in diurnal and seasonal ΔT over 15 years and the summer daytime spatio-temporal variation in urban built-up LSTs. The temporal analysis of ΔT showed that urbanization, quantified using ISA data, and ΔT increased in both countries over 15 years. This increase in ΔT over time is higher in India than in the US during nighttime and is statistically significant. However, the daytime ΔT change between the two countries is not statistically significant. The summer daytime urban built-up LST analysis showed that the recent (2007-2016) built-up areas in India were warmer than those older (before 2007). However, the reverse pattern occurs in the US over the same periods. Further, cluster analysis of urban built-up LSTs showed that green vegetation, with a Normalized Difference Vegetation Index (NDVI) greater than 0.3, reduces the LST of the neighboring built-up areas. This reduction in built-up area LST with a green neighbor is higher in the US than in India.

This study indicates that rural areas are not consistently cooler than urban areas in India, although that assumption is implicit in the definition of UHI. Additionally, the current global SUHI research methods need revisions for locations where drought or other factors may result in non-green vegetation. This study's methodological approach showed the variation in urban LSTs with builtup areas and vegetation, which was not apparent through conventional methods. Such an approach can also be relevant for other countries with similar characteristics. The study's quantitative findings show the need and scope to improve urban surfaces and buildings to reduce urban LSTs. Although the results indicate green vegetation as a potential UHI mitigation strategy, its effectiveness needs evaluation in conjunction with limitations on water availability and overall urban densities. The study also emphasized the need for more localized SUHI research methods and measures that can specifically analyze the impact of the urban built environment on urban LSTs.